A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Jessica Fridrich, Jan Kodovský
This paper introduces a novel strategy for building steganalysis detectors by assembling a rich model of noise residuals and using ensemble classifiers, achieving high detection accuracy against spatial-domain steganography.
Zhi‐Hua Zhou
A comprehensive textbook covering ensemble methods including Boosting, Bagging, Random Forest, and diversity measures.
Robi Polikar
This paper reviews ensemble-based systems in decision making, covering conditions for their superiority over single classifiers, algorithms like bagging and boosting, combination rules, and future applications.
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, et al.
This paper demonstrates that residual connections accelerate Inception network training and improve accuracy, achieving 3.08% top-5 error on ImageNet with an ensemble.
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, et al.
Comprehensive review of uncertainty quantification methods in deep learning, covering Bayesian and ensemble techniques across vision, NLP, and reinforcement learning.
Noah Hollmann, Samuel Müller, Lennart Purucker, et al.
TabPFN, a transformer-based tabular foundation model, outperforms all prior methods on small datasets (up to 10k samples) in 2.8 seconds, surpassing tuned ensembles trained for hours.